%I=imread(str);
function T = GLCM(I)
G=rgb2gray(I);
[M,N] = size(G);
%2.为了减少计算量,对原始图像灰度级压缩,将Gray量化成16级
for i = 1:M
for j = 1:N
for n = 1:256/16
if (n-1)*16<=G(i,j) && G(i,j)<=(n-1)*16+15
G(i,j) = n-1;
end
end
end
end
%3.计算四个共生矩阵P,取距离为1,角度分别为0,45,90,135
%--------------------------------------------------------------------------
P = zeros(16,16,4);
for m = 1:16
for n = 1:16
for i = 1:M
for j = 1:N
if j<N && G(i,j)==m-1 && G(i,j+1)==n-1
P(m,n,1) = P(m,n,1)+1;
P(n,m,1) = P(m,n,1);
end
if i>1 && j<N && G(i,j)==m-1 && G(i-1,j+1)==n-1
P(m,n,2) = P(m,n,2)+1;
P(n,m,2) = P(m,n,2);
end
if i<M && G(i,j)==m-1 && G(i+1,j)==n-1
P(m,n,3) = P(m,n,3)+1;
P(n,m,3) = P(m,n,3);
end
if i<M && j<N && G(i,j)==m-1 && G(i+1,j+1)==n-1
P(m,n,4) = P(m,n,4)+1;
P(n,m,4) = P(m,n,4);
end
end
end
if m==n
P(m,n,:) = P(m,n,:)*2;
end
end
end
% 对共生矩阵归一化
%%---------------------------------------------------------
for n = 1:4
P(:,:,n) = P(:,:,n)/sum(sum(P(:,:,n)));
end
%4.对共生矩阵计算能量、熵、惯性矩、相关4个纹理参数
%--------------------------------------------------------------------------
H = zeros(1,4);
I = H;
Ux = H;
Uy = H;
deltaX= H;
deltaY = H;
C=H;
for n=1:4
E(n) = sum(sum(P(:,:,n).^2)); %%能量
for i = 1:16
for j = 1:16
if P(i,j,n)~=0
H(n) = -P(i,j,n)*log(P(i,j,n))+H(n); %%熵
end
I(n) = (i-j)^2*P(i,j,n)+I(n); %%惯性矩
Ux(n) = i*P(i,j,n)+Ux(n); %相关性中μx
Uy(n) = j*P(i,j,n)+Uy(n); %相关性中μy
end
end
end
for n = 1:4
for i = 1:16
for j = 1:16
deltaX(n) = (i-Ux(n))^2*P(i,j,n)+deltaX(n); %相关性中σx
deltaY(n) = (j-Uy(n))^2*P(i,j,n)+deltaY(n); %相关性中σy
C(n) = i*j*P(i,j,n)+C(n);
end
end
C(n) = (C(n)-Ux(n)*Uy(n))/deltaX(n)/deltaY(n); %相关性
end
A1=[E(1) E(2) E(3) E(4)];
A2=[H(1) H(2) H(3) H(4)];
A3=[I(1) I(2) I(3) I(4)];
A4=[C(1) C(2) C(3) C(4)];
%求能量、熵、惯性矩、相关的均值和标准差作为最终8维纹理特征
%--------------------------------------------------------------------------
a1 = mean(A1);
b1 = sqrt(cov(A1));
a2 = mean(A2);
b2 = sqrt(cov(A2));
a3 = mean(A3);
b3 = sqrt(cov(A3));
a4 = mean(A4);
b4 = sqrt(cov(A4));
T=[];
T=[a1 b1 a2 b2 a3 b3 a4 b4];